Update app.py

app.py

@@ -55,6 +55,10 @@ def gradio_generate_text(prompt, max_length=100, num_return_sequences=1, top_p=0
     generated_text = generate_text(tokenizer, model, device, prompt, max_length, num_return_sequences, top_p, temperature)
     return generated_text
 
+def gradio_generate_text(prompt, max_length, num_sequences, top_p, temperature):
+    # Placeholder for your text generation logic
+    return f"Generated text based on: {prompt}"
+
 # Ensure the models directory exists
 if not os.path.exists('models'):
     os.makedirs('models')
@@ -69,19 +73,17 @@ model_path = "models/vi-medical-t5-finetune-qa"
 tokenizer, model, device = load_model_and_tokenizer(model_path)
 print('dqwdqqqqqqqqqqqqqqqqq_2')
 # Create Gradio interface
-def gradio_generate_text(prompt, max_length, num_sequences, top_p, temperature):
-    # Placeholder for your text generation logic
-    return f"Generated text based on: {prompt}"
+
 
 # Create the Gradio interface
 iface = gr.Interface(
     fn=gradio_generate_text,
     inputs=[
         gr.Textbox(lines=5, label="Input Prompt"),
-        gr.Slider(minimum=10, maximum=500, default=100, label="Max Length"),
-        gr.Slider(minimum=1, maximum=10, default=1, label="Number of Sequences"),
-        gr.Slider(minimum=0.1, maximum=1.0, default=0.95, label="Top-p Sampling"),
-        gr.Slider(minimum=0.1, maximum=1.0, default=0.7, label="Temperature")
+        gr.Slider(minimum=10, maximum=500, value=100, label="Max Length"),
+        gr.Slider(minimum=1, maximum=10, value=1, label="Number of Sequences"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, label="Top-p Sampling"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature")
     ],
     outputs=gr.Textbox(label="Generated Text"),
     title="Vietnamese Medical T5 Fine-Tuned Model",
@@ -89,4 +91,4 @@ iface = gr.Interface(
 )
 
 # Launch the Gradio interface
-iface.launch()
+iface.launch()